fix resuming from checkpoint for fault-tolerant in case of no failure (#9371)

Co-authored-by: Justus Schock <[email protected]>

Author: awaelchli

CHANGELOG.md

@@ -23,8 +23,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 
 - Progress tracking
-    * Integrate `TrainingEpochLoop.total_batch_idx` ([#8598](https://github.com/PyTorchLightning/pytorch-lightning/pull/8598)
-    * Avoid optional `Tracker` attributes ([#9320](https://github.com/PyTorchLightning/pytorch-lightning/pull/9320)
+    * Integrate `TrainingEpochLoop.total_batch_idx` ([#8598](https://github.com/PyTorchLightning/pytorch-lightning/pull/8598))
+    * Avoid optional `Tracker` attributes ([#9320](https://github.com/PyTorchLightning/pytorch-lightning/pull/9320))
+    * Reset `current` progress counters when restarting an epoch loop that had already finished ([#9371](https://github.com/PyTorchLightning/pytorch-lightning/pull/9371))
 
 
 - Added `batch_size` and `rank_zero_only` arguments for `log_dict` to match `log` ([#8628](https://github.com/PyTorchLightning/pytorch-lightning/pull/8628))

pytorch_lightning/loops/epoch/training_epoch_loop.py

@@ -98,7 +98,7 @@ def reset(self) -> None:
         # track epoch output
         self._epoch_output = [[] for _ in range(self.batch_loop.num_active_optimizers(self.total_batch_idx))]
 
-        if not self.restarting:
+        if not self.restarting or self._num_training_batches_reached():
             self.batch_progress.current.reset()
             self.scheduler_progress.current.reset()
             self.batch_loop.optimizer_loop.optim_progress.reset_on_epoch()

pytorch_lightning/loops/fit_loop.py

@@ -263,7 +263,7 @@ def teardown(self) -> None:
 
     def on_save_checkpoint(self) -> Dict:
         state_dict = super().on_save_checkpoint()
-        # FIXME(@tchaton) Should pass has_completed=True when iterator is exhausted ?
+        # TODO: update has_completed to its proper value
         state_dict["dataloader_state_dict"] = self.trainer.train_dataloader.state_dict(has_completed=False)
         return state_dict
 

pytorch_lightning/loops/optimization/optimizer_loop.py

@@ -63,7 +63,7 @@ def connect(self, **kwargs: "Loop") -> None:
         raise NotImplementedError(f"{self.__class__.__name__} does not connect any child loops.")
 
     def reset(self) -> None:
-        if not self.restarting:
+        if not self.restarting or self.done:
             self.optim_progress.optimizer_idx = 0
         self.outputs = [[] for _ in range(len(self.trainer.optimizers))]
 

tests/loops/test_loops.py

@@ -22,6 +22,7 @@
 import torch
 
 from pytorch_lightning import Trainer
+from pytorch_lightning.callbacks import ModelCheckpoint
 from pytorch_lightning.loops import Loop, TrainingBatchLoop
 from pytorch_lightning.trainer.progress import BaseProgress
 from tests.helpers import BoringModel
@@ -513,3 +514,235 @@ def configure_optimizers_multiple(self):
     assert state_dict != checkpoint["loops"]["fit_loop"]
     assert state_dict["epoch_progress"]["total"]["started"] == stop_epoch + 1
     assert state_dict["epoch_progress"]["current"]["started"] == stop_epoch
+
+
+@mock.patch.dict(os.environ, {"PL_FAULT_TOLERANT_TRAINING": "1"})
+@pytest.mark.parametrize("n_optimizers", (1, 3, 5))
+@RunIf(min_torch="1.7.0")
+def test_loop_state_on_complete_run(n_optimizers, tmpdir):
+    n_epochs = 3
+    n_batches = 3
+    accumulate_grad_batches = 1
+
+    class TestModel(BoringModel):
+        def __init__(self):
+            super().__init__()
+            if n_optimizers > 1:
+                self.configure_optimizers = self.configure_optimizers_multiple
+
+        def training_step(self, batch, batch_idx, optimizer_idx=0):
+            return super().training_step(batch, batch_idx)
+
+        def configure_optimizers_multiple(self):
+            optimizers = [torch.optim.Adam(self.layer.parameters(), lr=0.1) for _ in range(n_optimizers)]
+
+            lr_scheduler_0 = torch.optim.lr_scheduler.StepLR(optimizers[0], step_size=1)
+            lr_scheduler_1 = torch.optim.lr_scheduler.StepLR(optimizers[1], step_size=1)
+            # no scheduler for optimizer_2
+            lr_schedulers = [lr_scheduler_0, {"scheduler": lr_scheduler_1, "interval": "step"}]
+
+            return optimizers, lr_schedulers
+
+    model = TestModel()
+    model.training_epoch_end = None
+
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_epochs=n_epochs,
+        limit_train_batches=n_batches,
+        limit_val_batches=0,
+        accumulate_grad_batches=accumulate_grad_batches,
+        progress_bar_refresh_rate=0,
+        logger=False,
+        checkpoint_callback=True,
+    )
+    trainer.fit(model)
+
+    ckpt_path = trainer.checkpoint_callback.best_model_path
+    assert os.path.exists(ckpt_path)
+    checkpoint = torch.load(ckpt_path)
+
+    n_sch_steps_total = n_epochs
+    n_sch_steps_current = 1
+    if n_optimizers > 1:
+        n_sch_steps_total = n_epochs + n_epochs * n_batches
+        n_sch_steps_current = n_batches + 1
+
+    expected = {
+        "state_dict": ANY,
+        "epoch_progress": {
+            "total": {
+                "ready": n_epochs,
+                "started": n_epochs,
+                "processed": n_epochs,
+                # TODO: the following "-1" offset will be fixed by
+                #   https://github.com/PyTorchLightning/pytorch-lightning/pull/8578
+                "completed": n_epochs - 1,
+            },
+            "current": {
+                "ready": n_epochs,
+                "started": n_epochs,
+                "processed": n_epochs,
+                # TODO: the following "-1" offset will be fixed by
+                #   https://github.com/PyTorchLightning/pytorch-lightning/pull/8578
+                "completed": n_epochs - 1,
+            },
+        },
+        "epoch_loop.state_dict": ANY,
+        "epoch_loop.batch_progress": {
+            "total": {
+                "ready": n_epochs * n_batches,
+                "started": n_epochs * n_batches,
+                "processed": n_epochs * n_batches,
+                "completed": n_epochs * n_batches,
+            },
+            "current": {
+                "ready": n_batches,
+                "started": n_batches,
+                "processed": n_batches,
+                "completed": n_batches,
+            },
+        },
+        "epoch_loop.scheduler_progress": {
+            "total": {"ready": n_sch_steps_total, "completed": n_sch_steps_total},
+            "current": {"ready": n_sch_steps_current, "completed": n_sch_steps_current},
+        },
+        "epoch_loop.batch_loop.state_dict": ANY,
+        "epoch_loop.batch_loop.manual_loop.state_dict": ANY,
+        "epoch_loop.batch_loop.optimizer_loop.state_dict": {},
+        "epoch_loop.batch_loop.optimizer_loop.optim_progress": {
+            "optimizer_idx": n_optimizers,
+            "optimizer": {
+                "step": {
+                    "total": {
+                        "ready": n_epochs * n_batches * n_optimizers,
+                        "completed": n_epochs * n_batches * n_optimizers,
+                    },
+                    "current": {
+                        "ready": n_batches * n_optimizers,
+                        "completed": n_batches * n_optimizers,
+                    },
+                },
+                "zero_grad": {
+                    "total": {
+                        "ready": n_epochs * n_batches * n_optimizers,
+                        "started": n_epochs * n_batches * n_optimizers,
+                        "completed": n_epochs * n_batches * n_optimizers,
+                    },
+                    "current": {
+                        "ready": n_batches * n_optimizers,
+                        "started": n_batches * n_optimizers,
+                        "completed": n_batches * n_optimizers,
+                    },
+                },
+            },
+        },
+        "epoch_loop.val_loop.state_dict": ANY,
+        "epoch_loop.val_loop.dataloader_progress": ANY,
+        "epoch_loop.val_loop.epoch_loop.state_dict": ANY,
+        "epoch_loop.val_loop.epoch_loop.batch_progress": ANY,
+        "epoch_loop.val_loop._results": ANY,
+        "epoch_loop._results": ANY,
+    }
+    assert checkpoint["loops"]["fit_loop"] == expected
+
+
+@mock.patch.dict(os.environ, {"PL_FAULT_TOLERANT_TRAINING": "1"})
+@RunIf(min_torch="1.7.0")
+def test_fit_loop_reset(tmpdir):
+    """Test that the reset logic in fit- and epoch loop is aware of whether the loop is restarting from a completed
+    loop or from a mid-epoch checkpoint."""
+
+    # generate checkpoints at end of epoch and mid-epoch
+    model = BoringModel()
+    checkpoint_callback = ModelCheckpoint(
+        dirpath=tmpdir,
+        every_n_train_steps=2,
+        save_top_k=-1,
+    )
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        limit_train_batches=4,
+        num_sanity_val_steps=0,
+        max_epochs=2,
+        callbacks=[checkpoint_callback],
+        logger=False,
+        weights_summary=None,
+    )
+    trainer.fit(model)
+
+    # reset state loaded from a checkpoint from mid-epoch
+    mid_epoch_ckpt = torch.load(str(tmpdir / "epoch=0-step=1.ckpt"))
+    fit_loop = trainer.fit_loop
+    epoch_loop = fit_loop.epoch_loop
+    optimizer_loop = epoch_loop.batch_loop.optimizer_loop
+    assert not fit_loop.restarting
+    assert not epoch_loop.restarting
+    assert not optimizer_loop.restarting
+
+    fit_loop.load_state_dict(mid_epoch_ckpt["loops"]["fit_loop"])
+
+    def mid_epoch_reset_assertions():
+        assert fit_loop.restarting
+        assert fit_loop.epoch_progress.total.ready == 1
+        assert fit_loop.epoch_progress.total.completed == 0  # the checkpoint was saved mid epoch
+        assert fit_loop.epoch_progress.current.ready == 0
+        assert fit_loop.epoch_progress.current.completed == 0
+
+        assert epoch_loop.restarting
+        assert epoch_loop.batch_progress.total.ready == 2
+        assert epoch_loop.batch_progress.total.completed == 1  # the checkpoint was saved on train_batch_end
+        assert epoch_loop.batch_progress.current.ready == 2
+        assert epoch_loop.batch_progress.current.completed == 2
+
+    # resetting from a mid-epoch checkpoint should not change progress counters
+    mid_epoch_reset_assertions()
+    assert optimizer_loop.optim_progress.optimizer_idx == 1
+    fit_loop.reset()
+    epoch_loop.reset()
+    optimizer_loop.reset()
+    mid_epoch_reset_assertions()
+    assert optimizer_loop.optim_progress.optimizer_idx == 0
+
+    # reset state loaded from a checkpoint from the end of an epoch
+    end_of_epoch_ckpt = torch.load(str(tmpdir / "epoch=0-step=3.ckpt"))
+    fit_loop = trainer.fit_loop
+    epoch_loop = fit_loop.epoch_loop
+    fit_loop.restarting = False
+    epoch_loop.restarting = False
+    optimizer_loop.restarting = False
+
+    fit_loop.load_state_dict(end_of_epoch_ckpt["loops"]["fit_loop"])
+
+    assert fit_loop.restarting
+    assert fit_loop.epoch_progress.total.ready == 1
+    assert fit_loop.epoch_progress.total.completed == 0  # the checkpoint saves before the epoch completes
+    assert fit_loop.epoch_progress.current.ready == 0
+    assert fit_loop.epoch_progress.current.completed == 0
+
+    assert epoch_loop.restarting
+    assert epoch_loop.batch_progress.total.ready == 4
+    assert epoch_loop.batch_progress.total.completed == 3  # the checkpoint was saved on train_batch_end
+    assert epoch_loop.batch_progress.current.ready == 4
+    assert epoch_loop.batch_progress.current.completed == 4
+
+    assert optimizer_loop.optim_progress.optimizer_idx == 1
+
+    # resetting from a end-of-epoch checkpoint should reset the current counters to 0
+    fit_loop.reset()
+    epoch_loop.reset()
+    optimizer_loop.reset()
+
+    assert fit_loop.restarting
+    assert fit_loop.epoch_progress.total.ready == 1
+    assert fit_loop.epoch_progress.total.completed == 0  # the checkpoint saves before the epoch completes
+    assert fit_loop.epoch_progress.current.ready == 0
+    assert fit_loop.epoch_progress.current.completed == 0
+
+    assert epoch_loop.restarting
+    assert epoch_loop.batch_progress.total.ready == 4
+    assert epoch_loop.batch_progress.total.completed == 3  # the checkpoint was saved on train_batch_end
+    assert epoch_loop.batch_progress.current.ready == 0
+    assert epoch_loop.batch_progress.current.completed == 0
+
+    assert optimizer_loop.optim_progress.optimizer_idx == 0